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J Expo Sci Environ Epidemiol. 2018 Oct 9. doi: 10.1038/s41370-018-0079-0. [Epub ahead of print]

Verifying locations of sources of historical environmental releases of dioxin-like compounds in the U.S.: implications for exposure assessment and epidemiologic inference.

Author information

1
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, United States. rena.jones@nih.gov.
2
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States.
3
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
4
Westat Inc, Rockville, MD, United States.
5
Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.
6
JRN Environmental Health Sciences, North Bethesda, MD, United States.
7
TNO, Netherlands Organization for Applied Scientific Research, The Hague, Netherlands.
8
Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
9
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, United States.

Abstract

Polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/F) emissions from industrial sources contaminate the surrounding environment. Proximity-based exposure surrogates assume accuracy in the location of PCDD/F sources, but locations are not often verified. We manually reviewed locations (i.e., smokestack geo-coordinates) in a historical database of 4478 PCDD/F-emitting facilities in 2009 and 2016. Given potential changes in imagery and other resources over this period, we re-reviewed a random sample of 5% of facilities (n = 240) in 2016. Comparing the original and re-review of this sample, we evaluated agreement in verification (location confirmed or not) and distances between verified locations (verification error), overall and by facility type. Using the verified location from re-review as a gold standard, we estimated the accuracy of proximity-based exposure metrics and epidemiologic bias. Overall agreement in verification was high (>84%), and verification errors were small (median = 84 m) but varied by facility type. Accuracy of exposure classification (≥1 facility within 5 km) for a hypothetical study population also varied by facility type (sensitivity: 69-96%; specificity: 95-98%). Odds ratios were attenuated 11-69%, with the largest bias for rare facility types. We found good agreement between reviews of PCDD/F source locations, and that exposure prevalence and facility type may influence associations with exposures derived from this database. Our findings highlight the need to consider location error and other contextual factors when using proximity-based exposure metrics.

PMID:
30302014
DOI:
10.1038/s41370-018-0079-0

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